Q: Reason for writing the book?
A: In 2000 I was doing my Ph.D.
at Oxford University on the subject of weather forecasting. Everyone
knows that weather forecasts tend to go wrong after a couple of
days. At the time, the dominant theory was that the errors were
caused by chaos - the so-called "butterfly
effect." My work showed that actually the problem was
errors in the model. The scientists were using chaos as a kind
of fig leaf to explain bad forecasts. This raised for me a number
of questions about the science and sociology of forecasting, which
this book attempts to answer. Also, while there are many books
about predictions, there are rather fewer by people with direct
knowledge and experience of mathematical models.

Q: Is the book very mathematical?
A: No, it does not use equations,
except a few in the appendices, and it is aimed at the general
reader. The book is divided in three parts. The first is a history
of prediction, from the oracles of ancient Greece on. The second
part discusses how predictions are currently made in the areas
of weather, genetics, and economics. The final part looks at how
the three combine in long-term forecasts for the planet.

Q: What do areas like weather, health,
and economics have in common?
A: The three areas of prediction
share a similar history and use very similar methods. The systems
being predicted - the atmosphere, the human body, and the economy
- also have a similar level of complexity. Finally, the three
are also frequently interrelated - Hurricane Katrina caused an
economic crisis in New Orleans, as well as fears of a disease
outbreak. A growing world economy is more likely to produce climate
change which enhances the spread of disease-causing insects. So
understanding one area of prediction helps understand the others.

Q: Why are we so fascinated by predictions?
A: Humans are hard-wired to be interested
in predictions, because the ability to think about the future
is vital for our survival. However we are not good at going back
to check whether the predictions are accurate - which is why there
are so many well-paid forecasters in areas like economics, despite
their poor track record of success. There is a similar demand
for predictions in fields such as genetics or health. In 2006
many forecasters believed that avain flu would cause the next
deadly pandemic, but fortunately that has not yet happened. False
alarms can be good if they motivate us to protect ourselves against
future threats.

Q: Why is it so hard to make accurate
forecasts?
A: The predictions are made using
mathematical models, which suffer from two problems. The first
is that they cannot capture the full detail of the underlying
system, so rely on approximate equations. The second is that they
are sensitive to small changes in the exact form of these equations.
This is because complex systems like the economy or the climate
consist of a delicate balance of opposing forces, so a slight
imbalance in their representation has big effects. The models
can be adjusted to fit past data, but still fail to predict the
future. Increasing the size of the model doesn't necessarily help,
because the number of unknown parameters just increases. This
is why predictions of things like economic recessions are still
highly inaccurate, despite the use of enormous models running
on fast computers.

Q: What is Apollo's arrow?
A: According to legend, the Greek
philosopher Pythagoras was sired by Apollo, the god of prophecy
who provided the predictions for the Delphic oracle. At one point,
Pythagoras was presented with an arrow said to have belonged to
Apollo, which had magical powers that allowed him to dart across
space and time, cure plagues, and so on. He founded a school -
really a cult - that taught prediction using numbers, and worshipped
Apollo. Many regard the Pythagoreans, as they were known, as the
founders of Western science. So Apollo's arrow is a metaphor in
the book for numerical prediction. Today we use mathematical models
to dart into the future.

Q: Of the different kinds of prediction
discussed in the book, climate change is the most contentious.
Is there a connection between predictions of the short-term weather,
and the long-term climate? A: It is often said that predicting
the climate is much easier than predicting the weather. However
both types of prediction are based on similar models, and suffer
from the same kinds of model errors. One of the largest sources
of error is clouds. Cloud formation is a complex process that
depends on myriad local interactions between water vapour, air,
and microscopic particles that act as seeds. This cannot be precisely
modelled - there is no equation for a cloud. Modellers therefore
use approximate formulas. This is why predictions of rainfall
and other precipitation are so unreliable. Because clouds also
play a key role in regulating the climate, climate predictions
are also sensitive to small changes in the way clouds are represented.

Q: Can scientists accurately predict
climate change?
A: No. One indicator is that in
recent decades there has been amazingly little progress in prediction
accuracy. Back in 1979, some climate scientists met to estimate
the likely effects of doubling the level of carbon dioxide in
the atmosphere. They guess-timated a range of 1.5-4.5ºC,
with an average of 3ºC. The Intergovernmental Panel on Climate
Change (IPCC) was founded to refine the result using advanced
mathematical models. Their most recent summary
statement concludes that "It is likely to be in the range
2 to 4.5°C with a best estimate of about 3°C, and is very
unlikely to be less than 1.5°C." So there has been no
improvement in 28 years, despite huge increases in technology,
computers, and the number of scientists. The range really represents
a kind of fuzzy, social consensus in the climate community, which
is heavily influenced by precedent (as the book shows, the uncertainty
in the models themselves is even greater). When different economic
scenarios are taken into account, the IPCC's final range is about
1-7°C, which barely qualifies as a prediction - either there
will be little change, or it's the end of the world. Climate scientists
have become very good at understanding the current climate; but
their forecasts for the future are trying to be both incredibly
vague and authoritative at the same time, which in the end just
confuses people.

Q: Does the book argue then that climate
change is not a problem?
A: No, I believe that we are having
a dangerous effect on the climate, but I also believe that we
can't predict its future. Most environmentalists gloss over the
problems in the models. This is dangerous for two reasons. The
first is that bad science will never convince skeptics. The second,
more subtle reason is that the emphasis on mechanistic models
and technology perpetuates the idea that we can predict and control
the planet, which is what got us into trouble in the first place.
So what we need is a different, more humble approach. As I discuss
in the book, many environmentalists embrace Gaia theory (named
for the Greek Earth goddess) which states that the Earth can be
viewed as a living organism. If the Earth is alive, that makes
it deserving of our protection, but at the same time rather unpredictable.
So there's no contradiction in believing that climate change is
likely to be a problem, but is beyond our computational abilities.
Interestingly, according to legend, the prophecies at the Delphic
oracle were initially read out by Gaia, before the oracle was
taken over by Apollo.

Q: If it is so hard to predict the future,
then how can we make a decision about issues like whether we should
cut back on carbon emissions?
A: In most areas of life we don't
rely on complex mathematical models to make decisions. For example,
if you are choosing whether to vote for a particular politician,
you can't predict exactly what they will do when they get in office,
but you can still make an informed judgement and vote accordingly.
Another example is diet. If a child is eating a lot of candy bars,
and is putting on weight, then we might suggest they cut back
- even if we can't mathematically prove that they will become
dangerously overweight or develop diabetes. Carbon dioxide is
like sugar for the planet - it boosts plant growth and makes the
climate warmer - and observations show that our emissions are
having a dangerous effect. In any case, our environmental problems
extend far beyond global warming, and there are many reasons to
reduce our impact on the planet. The realization that we are out
of our depth may open the way to a more immediate and direct response.
As I discuss in the book, mathematical models are powerful and
essential tools in many areas of science, and they help us understand
complex systems like the weather, the economy, or our own bodies.
But we shouldn't rely on them to predict the future.